Area of research
Safety, Risk, Reliability and Quality · Civil and Structural Engineering
Research interest
Research interests include Computer science, Random field, Engineering, Rock mass classification, Field (mathematics), and Geotechnical engineering.
Prediction model of TBM response parameters based on a hybrid drive of knowledge and data
Bayesian sequential learning of rock mass classifications along tunnel trajectory using TBM operational data and geo-data spatial correlation
Physics-informed and data-driven machine learning of rock mass classification using prior geological knowledge and TBM operational data
Data-Driven Development of Three-Dimensional Subsurface Models from Sparse Measurements Using Bayesian Compressive Sampling: A Benchmarking Study
A data driven real-time perception method of rock condition in TBM construction
Machine learning of geological details from borehole logs for development of high-resolution subsurface geological cross-section and geotechnical analysis
Mechanical response estimation of jointed rigid pipes under normal fault rupture
Simulation of Random Fields with Trend from Sparse Measurements without Detrending
Direct simulation of random field samples from sparsely measured geotechnical data with consideration of uncertainty in interpretation
Some observations on ISO2394:2015 Annex D (Reliability of Geotechnical Structures)
Bayesian identification of random field model using indirect test data
Simultaneous Bayesian Sparse Approximation With Structured Sparse Models